The present invention relates generally to magnetic resonance (MR) imaging and, more particularly, to a process for updating a specific absorption rate (SAR) prediction model for data acquisition of a given subject based on determined SAR characteristics of the given subject.
When a substance such as human tissue is subjected to a uniform magnetic field (polarizing field B0), the individual magnetic moments of the spins in the tissue attempt to align with this polarizing field, but precess about it in random order at their characteristic Larmor frequency. If the substance, or tissue, is subjected to a magnetic field (excitation field B1) which is in the x-y plane and which is near the Larmor frequency, the net aligned moment, or “longitudinal magnetization”, MZ, may be rotated, or “tipped”, into the x-y plane to produce a net transverse magnetic moment Mt. A signal is emitted by the excited spins after the excitation signal B1, which is realized by an RF pulse, is terminated and this signal may be received and processed to form an image.
When utilizing these signals to produce images, magnetic field gradients (Gx, Gy, and Gz) are employed. Typically, the region to be imaged is scanned by a sequence of measurement cycles in which these gradients vary according to the particular localization method being used. The resulting set of received NMR signals are digitized and processed to reconstruct the image using one of many well known reconstruction techniques.
High field MR scanners, for example, 3T and above, are designed to continuously monitor RF energy deposited in a subject. In particular, the RF energy absorbed per unit of mass of the subject, or SAR, is monitored during a scan. When measured SAR reaches a prescribed limit, scanning is interrupted. To assist prescription of the scan to avoid an SAR tripping level, an RF prediction model is generally used to guide the user in setting sequence parameter limits such that the prescribed scan has a high probability for completion without exceeding RF limits. This model is typically based on RF energy measurements collected or modeled from a set of volunteers or phantoms and is generally tied to the weight of the subject. RF absorption equivalent mass (SAR mass) is assumed to be the weight of the subject. Two parameters that are optimized in the model include scan time for required number of slices and the probability of the scan completing without causing an RF limit trip or interruption.
Generally, the weight of the patient is a good approximation of the SAR mass. However, it is possible for the SAR mass of the given patient to be inaccurate. For example, a more muscular patient would have more SAR mass than a less muscular patient for the same weight. This SAR mass translates into a higher RF threshold for the patient relative to the predicted RF threshold. Conversely, if the patient SAR mass predicted is greater than the actual SAR mass of the patient, the prediction model may guide prescription to a scan protocol with excessive SAR for the patient.
Power monitors are used during the scan to interrupt the scan if measured SAR becomes excessive. The combination of the prediction model and the use of power monitors helps to ensure that patient mass errors do not compromise SAR limits. However, if the SAR mass of the patient is inaccurately predicted, the power monitor may unnecessarily trip based on a poorly calculated scan. On the other hand, the prediction model may over-estimate RF deposition in the patient and result in an under-powered scan.
It would therefore be desirable to have a system and method capable of allowing for refinement of the RF prediction model that is insensitive to patient particulars.